Since the term was coined in 1955, artificial intelligence has often been used as a metaphor for the dehumanizing growth of technology from a servant into a master. And yet, it's all around us now. Rather than the HAL 9000 computer ominously warning, "I'm sorry, Dave. I'm afraid I can't do that," AI today is taking a less sinister form, in the guise of Amazon's Alexa personal assistant and Tesla's self-driving cars. So it should come as no surprise that the incentive, reward and recognition industry is looking at how AI and the tools that go into it, like machine learning and predictive analytics, can be used to engage, recognize employees, channel partners and consumers.
"We are on the cusp of really understanding what AI and predictive analytics can tell us about motivating a population," says Melissa Van Dyke, president of the Incentive Research Foundation.
Why Artificial Intelligence Is the Next Motivation Strategy
Within the incentive industry, Maritz Motivation Solutions has led the way toward tapping the full potential of AI. Its Decision Sciences division, launched in 2017, puts predictive modeling and machine learning at the center of its offerings. The effort takes Maritz' ongoing work in neuroscience and behavioral economics and applies it to a system that learns more with each bit of data fed to it, refining its predictions of consumer behavior and employee preferences over time.
"Machine learning understands your preferences at a different level," says Maritz Motivation Solutions' chief data officer Jesse Wolfersberger, who heads up the Decision Sciences division. "We all get a ton of spam emails, but the ones that are really relevant can cut through that. If the messaging is relevant to the recipients, it should feel like we are shopping for them."
The efforts have already borne fruit: A pilot program for HSBC bank's credit-card loyalty program yielded high response rates, with 70 percent of the recipients who redeemed their points choosing rewards that were recommended to them by the algorithm. The ambitious program was so successful that it was chosen as the overall winner of Incentive's Grand Motivation Master award this year.
Maritz also is leveraging the technology in areas like forecasting auto-dealership sales and understanding employee engagement. "In the auto industry, retention is a big problem, so understanding when someone is about to leave or identifying strong sellers early can be valuable," says Wolfersberger.
Though Wolfersberger acknowledges that it is still very early days for the technology, he says the potential is undeniable. Other industry watchers agree, and that was one of the conclusions of a 2018 IRF study on AI and predictive analysis.
Reporting on the success and impact of these programs has become table stakes for third-party performance-improvement companies, says Mike McWilliams, vice president of marketing and client strategy for MotivAction. And once you've reported on the outcomes, the next step is gathering "satisfaction data," he explains. "Beyond that, it starts to progress toward the predictive — creating a database of what will work for employees, leveraging the inputs from every program. Then you've got to get an experience-design expert involved, someone who can read and analyze the data and create a strategy out of it."
That, McWilliams says, is the first step on the road to incorporating predictive analytics and AI into incentive, recognition and reward programs: "Being able to predict what moves the needle is the Holy Grail."
Why AI Matters for Consumer and Employee Incentives
What really makes AI and predictive analytics game changers, according to Jane Larson, SCIP, PMC, isn't just that they will help companies motivate employees to perform better, or customers to buy more.
The research manager of marketing strategy for incentive and engagement firm ITA Group thinks predictive analytics will "help us validate those best practices, but it might actually disrupt some of those beliefs and challenge us to make some changes based on data. ROI is always top of mind, and having the ability to leverage predictive analytics certainly gives us an opportunity to provide a better context around return on investment or return on objective."
Larson adds that program-participant data will "paint a picture for us of their behavior and personal or individual likes and preferences, and also their performance levels. I think as businesses come to understand that better, they're going to devise ways to better understand their sales forces and their customers."
Personalizing Incentives Using Technology
It is this individualization of reward and recognition offerings that has the potential to revolutionize motivation programs, says Allan Schweyer, the IRF's chief academic advisor. "You can talk about Millennials versus baby boomers, and you can talk about salespeople versus nonsalespeople and try to come up with more motivating rewards that way, but lumping people into categories based on age or position is suspect," he notes. "Whereas artificial intelligence that really does look at patterns on an individual basis can, I think, get pretty accurate in determining the right reward for the right person."
The value of getting reward offerings away from what Schweyer calls the "one-to-many" approach, in which every program participant is awarded the same way, such as with a choice of merchandise from a catalog or an incentive trip, was made clear in the IRF and Incentive Marketing Association's study of Participant Award Experience Preferences.
"What we found in that study is that awards are a multifaceted experience comprised of not just the award itself or the experience that the person gets, but also a function of who delivers the award, how it's communicated to them and whether there's any other professional development that comes along with it," says Melissa Van Dyke. "Depending on the type and size of the award, and who you're speaking to, that could be 30 to 60 percent of what really drives the person's experience."
Using AI and predictive analytics can help incentive planners drill down beyond just which award the individual incentive earner wants and create the full award experience that would most motivate that particular person, she says.
The Human Factor Remain Important
It is important to remember that no matter how powerful a tool AI and predictive analytics turn out to be, they are meant to be used by human managers, not to take over for them.
"It doesn't replace the need for human involvement," Jane Larson says, adding that while predictive analytics like data and information can help us, "we still need to have people that can develop the insights and the ideas that come from that."
The IRF's Van Dyke agrees. "Artificial intelligence and predictive analytics don't replace our ability to review data and make informed decisions," she says. "They just give us better tools to do that with. Predictive analytics and AI are all based on patterns, and patterns exist in data, period. So how accurate you can predict or you can make conclusions on that data is a function of how much data is available."
Those tools get more useful as the amount of processed data gets bigger, Van Dyke adds, noting that even a few hundred salespeople take many actions that can allow companies to amass thousands of data points, and the sample size only grows with time.
One thing to be aware of is that there are ethics questions that arise when gathering all that data and allowing machines to parse it for meaning, Van Dyke says, adding that in the incentive business, it's important to be aware of the line between motivation and manipulation. That delicate line, she believes, can be summed up as, "Is our intent what's best for the employee? We know that an organization has needs to be met. Our job is to ensure that there's clarity around the work that's being performed, that it is toward the ultimate goal that we're striving for as an organization, and then we deploy incentives and rewards to ensure that employees feel rewarded and respected for those efforts."
Plenty of clients also are hesitant to embrace the new technology. When Maritz's Decision Sciences first launched with its AI focus, Wolfersberger found, "It seemed like science fiction to a lot of clients — they'd heard of it, but didn't quite know the power of it." He's since seen that resistance thaw, especially as AI has become widely embraced in so many areas beyond the incentive industry itself. Yet he emphasizes that he still sees this as "just the beginning for the technology."